Recognizing Events in Spatiotemporal Soccer Data

Victor Khaustov,Maxim Mozgovoy

doi:10.3390/app10228046

Abstract

Spatiotemporal datasets based on player tracking are widely used in sports analytics research. Common research tasks often require the analysis of game events, such as passes, fouls, tackles, and shots on goal. However, spatiotemporal datasets usually do not include event information, which means it has to be reconstructed automatically. We propose a rule-based algorithm for identifying several basic types of events in soccer, including ball possession, successful and unsuccessful passes, and shots on goal. Our aim is to provide a simple procedure that can be used for practical soccer data analysis tasks, and also serve as a baseline model for algorithms based on more advanced approaches. The resulting algorithm is fast, easy to implement, achieves high accuracy on the datasets available to us, and can be used in similar scenarios without modification.

Highlights

Sports analytics can serve as a rich source of data for a variety of machine-learning tasks
Resulting spatiotemporal datasets have been used in numerous research tasks, including similar play sequences retrieval [1] and identification of defensive strategies [2] in basketball, and shot prediction in tennis [3]
We show how the obtained results are connected with our initial goals, what the advantages and limitations of our approach are, and outline possible further research directions

Summary

Introduction

Available sports-related datasets range from collections of individual players’ performance indicators and historical game stats, to detailed event logs of particular matches. Most available spatiotemporal sports datasets, to the best of our knowledge, are organized as sequences of game field snapshots that include athletes’ locations at specific points of time. Sports provide a vast amount of diverse information, ranging from statistical tables reflecting the performance of individuals and teams to video streams of competitions. Different sources of information provide different types of data, and it is not always possible to combine them to obtain a comprehensive picture of certain phenomena. The reasons for such fragmentation are both technological and legal.

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